1. Field of the Invention
This invention relates to multi-layered time-delayed neural networks useful in a variety of data and signal processing, image recognition and other computational tasks. In particular, the present invention relates to a means to convert serially encoded temporal firing intervals of a spike train waveform into a spatially distributed topographical matrix in which the interspike-interval and bandwidth information of the spike train may be extracted.
2. Description of the Related Technology
Topographical maps of neurons are found in the central nervous system of biological organisms for the tonotopical representation of tones, somatotopical representation of body surface, retinotopical representation of the visual world, etc. These topographical maps represent information based on the spatial locations of the neurons. The signals encoded by the biological neurons have characteristic pulse height and pulse width, and may be considered as pulse-coded signals where the information is encoded in the time of occurrence of the pulses.
This time-series of pulses is called a spike train and the neuron information is contained in the interspike-intervals between pulse firings from the neurons. Thus, the signal information transmitted by a neuron can be considered as "temporally-coded" by the time intervals between pulses in the spike train. Various methods of correlation analysis of spike trains in biological neurons have been developed. See for example "Neuronal spike trains and stochastic point process", Perkel, Gerstein and Moore, Biophys J., Vol. 7, pp. 391-440 (1967); "Cross-interval histogram and cross-interspike interval histogram correlation analysis of simultaneously recorded multiple spike train data", Tam, Ebner and Knox, Journal of Neuroscience Methods, Vol. 23, pp. 23-33 (1988).
Given this serial transmission of the temporally-coded interspike pulse train, the information contained within the spike intervals may be decoded into parallel topographically distributed codes. Topographical distribution of codes based on the location of the neurons is called "place-code". By converting temporally-coded signals into topographically distributed codes, the firing intervals of neurons may be readily recognized as a particular neuron in a population ensemble. Thus, individual firing patterns may be distinguished such as burst-firing from long-interval firing and periodic firing from non-periodic firing.
Neural networks are characterized by a high degree of parallelism between numerous interconnected simple processors. The neural network nomenclature is derived from the similarity of such networks to biological neural networks. See for example "Computing with Neural Circuits A Model", Hopfield and Tank, Vol. 233, pp. 622-33.
In general, neural networks are formed or modeled using a number of simple processors or neurons arranged in a highly interconnected pattern wherein each of the neurons performs the simple task of updating its output state based upon the value of signals presented to it as inputs. The design of a neural network involves determining the number, arrangement and weight of the interconnections between neurons. The weight of a connection corresponds to the biological synaptic strength and determines the degree in which the output signal on one neuron will effect the other neurons to which it is connected. Thus, each neuron or processor receives input signals which are derived from the output or activation states of other connected neurons.
These activation states or output signals are linearly, typically resistively, operated on via the connection weights and then summed. Summation may be accomplished in an analog circuit which adds together all input voltages to give a resultant voltage representative of the sum of the inputs. This input signal summation is then operated on by a non-linear processor function, such as a threshold detector, to produce an updated output activation state.